https://www.dagstuhl.de/21352

29. August – 01. September 2021, Dagstuhl-Seminar 21352

Higher-Order Graph Models: From Theoretical Foundations to Machine Learning

Organisatoren

Tina Eliassi-Rad (Northeastern University – Boston, US)
Vito Latora (Queen Mary University of London, GB)
Martin Rosvall (University of Umeå, SE)
Ingo Scholtes (Universität Würzburg, DE)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Jutka Gasiorowski zu administrativen Fragen

Michael Gerke zu wissenschaftlichen Fragen

Dagstuhl Reports

Wir bitten die Teilnehmer uns bei der notwendigen Dokumentation zu unterstützen und Abstracts zu ihrem Vortrag, Ergebnisse aus Arbeitsgruppen, etc. zur Veröffentlichung in unserer Serie Dagstuhl Reports einzureichen über unser
Dagstuhl Reports Submission System.

Dokumente

Teilnehmerliste
Gemeinsame Dokumente
Dagstuhl-Seminar Wiki
Programm des Dagstuhl-Seminars [pdf]

(Zum Einloggen bitte persönliche DOOR-Zugangsdaten verwenden)

Motivation

Graph and network models are a cornerstone of data science and machine learning applications in computer science, social sciences, humanities, and life sciences. Most state-of-the-art network analysis and graph-based learning techniques build on simple graph abstractions, where nodes represent a system's elements, and links represent dyadic interactions, relations, or dependencies between those elements. This mathematical formalism has proven useful for reasoning about, for example, the centrality of nodes, the evolution and control of dynamical processes, and the community or cluster structure in complex systems.

While the advantages of graph models of relational data are undisputed, we often have access to rich data with multiple types of higher-order, inherently non-dyadic interactions that simple graphs cannot represent in a meaningful way. Important examples include relational data on actors in social systems who engage in group collaborations, time-stamped interaction data giving rise to chronologically ordered sequences of (dyadic) links, sequential data such as user clickstreams, mobility trajectories or citation paths with sequences of traversed nodes, and data on networked systems with multiple types or layers of connectivity. Over the past years, researchers have shown that the presence of such higher-order interactions can fundamentally influence our understanding of complex networked systems. They can change our notion of the importance of nodes captured by centrality measures, affect the detection of cluster and community structures in graphs, and influence dynamical processes like diffusion or epidemic spreading, as well as associated control or containment strategies in non-trivial ways.

To address this important challenge, researchers in topological data analysis, network science, machine learning, and physics recently started to generalize network analysis to higher-order graph models that capture more than dyadic relations. Over the past few years, this research community has developed a rich portfolio of higher-order network models and representations. Exemplary modelling approaches successfully use hypergraphs, simplicial network models, high-dimensional De Bruijn graphs, higher-, variable-, and multi-order Markov chains, as well as multi-layer and multiplex networks. These modelling approaches address the same fundamental limitation of graph models, namely that we cannot understand the structure and dynamics of complex systems by decomposing direct and indirect interactions between elements into a set of dyadic relations with a single type. However, the similarities and differences between these different modelling approaches, and the machine learning techniques derived from them, are poorly understood.

Addressing this gap, this Dagstuhl Seminar aims to improve our understanding of the strengths, weaknesses, commonalities, and differences of these different approaches along with their resulting computational challenges. Bringing together key researchers from different communities, the seminar aims to form a common foundation for the developing graph mining and machine learning techniques that use recent advances in the study of higher-order graph models. We specifically aim to develop a common language and a shared research platform that fosters progress in data analytics and machine learning for data with complex relational structure.

Motivation text license
  Creative Commons BY 3.0 DE
  Tina Eliassi-Rad, Vito Latora, Martin Rosvall, and Ingo Scholtes

Classification

  • Data Structures And Algorithms
  • Machine Learning
  • Social And Information Networks

Keywords

  • Topological data analysis
  • (social) network analysis
  • Graph theory
  • Statistical relational learning
  • Graph mining

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von
Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.